A universal transfer learning framework for cross-working-condition marine diesel engine fault diagnosis based on fine-tuning strategy

Timely and accurate fault diagnosis (FD) of marine diesel engines (MDEs) is crucial for enhancing the safety and reliability of ship power systems. MDEs operate under variable conditions, leading to significant differences in their operational and fault data. This variability reduces the adaptability of data-driven FD models, which are developed using data from a single engine or specific conditions. To address the aforementioned issues, this study proposes a fault diagnosis framework for MDEs based on deep transfer learning and fine-tuning. To enhance the capability of fault feature extraction, a data-tiered fusion method is introduced for data reconstruction. Furthermore, a novel hybrid pre-training network combined CNN + GRU and KAN is proposed to obtain comprehensive source domain data features. Additionally, a combined fine-tuning strategy for the transfer of pre-trained models is presented to enable superior cross-working-condition fault knowledge sharing. Experiments validate the effectiveness of the framework using two typical MDEs fault datasets. The proposed framework achieves 95 % accuracy in the MDE cross-working-condition (CWC) FD task, which is significantly better than the non-transfer model. The proposed framework not only demonstrates excellent performance in CWC fault diagnosis but also provides new insights for the paradigm of MDEs fault diagnosis.